Genomics reveals the history of a complex plant invasion and improves the management of a biological invasion from the South African–Australian biotic exchange

Abstract Many plants exchanged in the global redistribution of species in the last 200 years, particularly between South Africa and Australia, have become threatening invasive species in their introduced range. Refining our understanding of the genetic diversity and population structure of native and alien populations, introduction pathways, propagule pressure, naturalization, and initial spread, can transform the effectiveness of management and prevention of further introductions. We used 20,221 single nucleotide polymorphisms to reconstruct the invasion of a coastal shrub, Chrysanthemoides monilifera ssp. rotundata (bitou bush) from South Africa, into eastern Australia (EAU), and Western Australia (WAU). We determined genetic diversity and population structure across the native and introduced ranges and compared hypothesized invasion scenarios using Bayesian modeling. We detected considerable genetic structure in the native range, as well as differentiation between populations in the native and introduced range. Phylogenetic analysis showed the introduced samples to be most closely related to the southern‐most native populations, although Bayesian analysis inferred introduction from a ghost population. We detected strong genetic bottlenecks during the founding of both the EAU and WAU populations. It is likely that the WAU population was introduced from EAU, possibly involving an unsampled ghost population. The number of private alleles and polymorphic SNPs successively decreased from South Africa to EAU to WAU, although heterozygosity remained high. That bitou bush remains an invasion threat in EAU, despite reduced genetic diversity, provides a cautionary biosecurity message regarding the risk of introduction of potentially invasive species via shipping routes.


| INTRODUC TI ON
The extensive intercontinental movement of people and products associated with globalization has facilitated biological invasions into managed and unmanaged environments and significantly threatens native biodiversity, economic and social values (Hulme, 2009;Mollot et al., 2017;Pyšek, Hulme, et al., 2020;Westphal et al., 2008). The risk of invasion and spread varies with introduction pathway (Riera et al., 2021;Wilson et al., 2009), propagule pressure (Blackburn et al., 2015;Simberloff et al., 2013), and genetic diversity of the founding population . Knowing the origin of introduced material also has implications for the success of classical biological control (Gaskin et al., 2011;Roderick & Navajas, 2003).
Consequently, there is a strong incentive to better understand the ecological context of the invasion to improve the efficiency and effectiveness of evidence-based management strategies (DiTomaso, 2000;Pheloung et al., 1999).

A critical component of improving management is understand-
ing early invasion stages, including introduction pathways, naturalization, and initial spread (Estoup & Guillemaud, 2010;Faulkner et al., 2020;Richardson et al., 2003). Detailed longitudinal studies of introduced pests and weeds are uncommon, particularly those that capture the introduction pathway and that include insight soon after their arrival into a new region. However, a history of the introduction can be inferred using approaches combining molecular genetics with traditional biogeographical theory. In particular, such studies can provide valuable insight to determine the origin of and reconstruct historical demographics for introduced populations (Cristescu, 2015;Kamenova et al., 2017). The understanding of historical invasion context achieved through molecular methods can help to predict, assess, and guide the effectiveness of management strategies (Chown et al., 2015;Sherpa & Despres, 2021).
Reconstruction of the invasion history and identification of source populations can highlight introduction pathways in order to prevent future introductions. This insight is particularly informative for target taxa with strong genetic structure or cryptic speciation in the native range, because divergent genotypes or cryptic species may differ in their susceptibility to biological control agents (Goolsby et al., 2005;Manrique et al., 2008;Paterson et al., 2019;Smith et al., 2018) or their suitability to the range of climatic niches available in the introduced range (Zenni et al., 2014). Molecular methods can also inform management by helping understand population connectivity via current and historical dispersal patterns (Nobarinezhad et al., 2020), delimiting populations for management or local extirpation (Hampton et al., 2004), and understanding the rate of invasion (i.e., expansion) for poorly documented introductions (Kamenova et al., 2017;Novak & Mack, 2001).
Insights into the genetic diversity of invasive species that can be gained through molecular methods can also provide insight into the propagule pressure of the invasion (Fitzpatrick et al., 2011).
Propagule pressure is an important factor in the successful establishment of invasive populations, through lessening effects of demographic stochasticity, Allee effects, environmental heterogeneity, genetic drift, and inbreeding depression (Blackburn et al., 2015;Simberloff et al., 2013). Mechanisms such as population admixture, introgressive hybridization, ongoing gene flow, and rapid population growth following bottlenecks of short duration can enable introduced populations to overcome large genetic loads (Bock et al., 2015;Colautti et al., 2017;Dlugosch & Parker, 2008;Kolbe et al., 2004;Prentis et al., 2008;Rius & Darling, 2014;Smith et al., 2020). In other cases, polyploidy provides a range of mechanisms for facilitating plant invasions, such as preadaptation to conditions in the introduced range, restoring sexual reproduction following hybridization, and conversely, overcoming Allee effects by enabling asexual reproduction (te Beest et al., 2012). Molecular analysis of introduced populations can determine propagule pressure and determine whether advantages gained by introduced species through evolutionary and demographic processes may hinder control attempts.
In the past decade, new genomics methods allowing researchers to examine thousands of loci at the population level have increased our power for understanding complex introduction pathways and evolution postintroduction. For example, studies of the invasive weed Centaurea solstitialis (Asteraceae) revealed a stepwise invasion history, starting with an early introduction into Western Europe from two source populations, which then served as a genetic bridgehead for invasions into Chile and then California, followed by evolution of increased plant size in the Californian population (Barker et al., 2017;Eriksen et al., 2014). Other studies have found that introduced populations have overcome bottlenecks and maintained genetic diversity through complex introduction histories involving multiple source populations (e.g., Frangula alnus; De Kort et al., 2016). Furthermore, by analyzing a large number of loci, it may be possible to determine population genetic diversity and differentiation using much smaller sample sizes than needed for traditional methods such as microsatellites (Qu et al., 2020). This insight is of particular relevance for studies of invasion history where many populations need to be analyzed.
There are few study systems where the value of this improved insight is more relevant to transforming our understanding of biotic invasions to enhance management outcomes than for the biotic exchange between Australia and South Africa . The exchange of invasive plants between Australia and South Africa contributes disproportionately to the most threatening of invasive species globally . These include both deliberate introductions for agricultural, ornamental, and environmental (e.g. dune stabilization) reasons (e.g., Asparagus

T A X O N O M Y C L A S S I F I C A T I O N
Biogeography; Evolutionary ecology; Genetics; Invasion ecology; Phylogenetics; Population genetics asparagoides; Morin et al., 2009) and numerous accidental introductions as contaminants of livestock, machinery, and ship ballast (e.g., Senecio madagascariensis; Wijayabandara et al., 2022). Many South Africa taxa, in particular the Iridaceae, appear to be on the verge of becoming invasive in Australia  and thus increasing the scale of the problem.
With multiple pathways of and rationales for introduction, the complexity of these invasion histories has made subsequent weed management particularly challenging. Considerable effort has been spent on managing the worst of the exchanged weeds in both countries, including eradication attempts (e.g., Chrysanthemoides monilifera ssp. rotundata;  and classical biological control programs (e.g., Asparagus asparagoides; Morin et al., 2022). However, both countries have ecosystems spanning a wide range of climates and many of the most problematic weeds occupy a broad realized niche (e.g., Lycium ferrocissium;McCulloch et al., 2020). This situation has made managing these species particularly difficult. For example, biological control programs have failed due to an apparent mismatch between agent and target biotypes, the most notable case is of the misidentification of Salvinia molesta (Julien, 2012), while multiple introductions of Acacia saligna in a range of global locations did not show a consistent pattern, indicating that invasion history can be very local (Thompson et al., 2015).
Applying new genomic techniques to the complex invasion management scenarios that characterize the biotic exchange between South Africa and Australia is likely to help solve management quandaries for multiple species, while also providing broader insight for mitigating future invasion risks.
To provide broader insight on the exchange of invasive weeds between South Africa and Australia and to focus in particular on a problematic study system, we investigated the invasion history of Chrysanthemoides monilifera ssp. rotundata (DC.) T.Norl. (bitou bush).
Bitou bush is a shrub native to coastal subtropical regions in the Eastern Cape and KwaZulu Natal provinces of South Africa. The plant was first recorded in Australia in 1908 near the port city of Newcastle, New South Wales (NSW), hypothesized to have been inadvertently introduced in dry ship's ballast (Weiss et al., 2008). The subsequent naturalization and invasion were accelerated between 1946 and 1968 when bitou bush was deliberately planted as a dune stabilizing species in coastal areas of NSW (Weiss et al., 2008). No evidence exists regarding the source of the seed used for these plantings (i.e., locally collected or single/multiple introductions from the native range). Bitou bush can now be found invading 44,000 hectares of coastal landscapes in eastern Australia (Hamilton et al., 2012). Due to its impact on the environment and high invasiveness, bitou bush is listed as a Weed of National Significance in Australia and has been subject to containment and localized extirpation efforts since 1982 (Cherry et al., 2008).
Until recently, it was thought that the Australian invasion was restricted to the east coast, largely within the state of New South Wales. However, a recent introduction leading to a naturalized population of about 1700 plants was discovered in 2012 in the coastal suburb of Kwinana, Western Australia (Scott et al., 2016).
This introduction was dated using aerial photography to 1995 and has been hypothesized to have originated from eastern Australia via shipping activity, given that a nearby port has links to the east coast of Australia (Scott & Batchelor, 2014;. However, as for the hypotheses for the east coast introduction, there is no published evidence for or against a shippingmediated introduction pathway. In an effort to better understand the introduction and invasion history as well as the genetic diversity of bitou bush in its non-native range, we applied de novo double digest restriction associated DNA sequencing (ddRADseq; Peterson et al., 2012) to generate SNPs from populations sampled in the native range of South Africa and the introduced range in Australia, and carried out flow cytometry analysis of the more recently introduced Western Australian population to assess the genome size and infer ploidy. Specifically, we aimed to (1) assess bitou bush population diversity, genetic structure, admixture, and ploidy; (2) compare hypothesized introduction scenarios for bitou bush using Bayesian modeling; and (3) using this insight, assess the likely effectiveness of current management strategies for bitou bush in Australia. These findings are placed into the broader context of how new genomic methods can be used to improve invasion management outcomes in general, and for the South African-Australian exchange of weeds in particular.

| Study species
The genus Chrysanthemoides belongs in the tribe Calenduleae of the Asteraceae family (Bayer & Starr, 1998;Norlindh, 1977). The genus comprises two species native to southern Africa with one, C. monilifera, divided into a variable number of infraspecific taxa (Barker et al., 2015) among which two (boneseed: C. monilifera ssp. monilifera and bitou bush: C. monilifera ssp. rotundata) have been introduced into Australia (Weiss et al., 2008). Bitou bush is found in coastal sand dunes and adjacent areas in a native range from subtropical regions near Cape St Francis to tropical regions near the South Africa-Mozambique border (Hamilton et al., 2012;Scott, 1996;Weiss et al., 2008). The closely related boneseed occupies coastal areas and further inland into the adjacent mountains in drier more Mediterranean climates of the south-western and south-eastern parts of South Africa (Weiss et al., 2008). Mature bitou bush shrubs vary in size from 0.5 to 2 m 2 canopy area in its native range (Scott, 1996), and up to 2-3 m high and wide in Australia (Scott, Batchelor, Jucker, & Webber, 2019; Figure 1). Reproduction is primarily by seed but can also include stem layering (Weiss, 1984). Unusually for members of Asteraceae, a bitou bush seed develops inside a fleshy fruit (i.e. drupe), which is consumed and dispersed by frugivorous species, chiefly birds (Gosper, 2004). This trait is expected to lead to a high capacity for dispersal and spread. Pollination has not been studied in detail, but appears to be by generalist insects (Weiss et al., 2008).  (Ens et al., 2009;Lindsay & French, 2004;Mason & French, 2008).

Management goals in the introduced Australian range vary
according to local context and include eradication, containment, and protection of sensitive environments. Management in eastern Australia is focussed on decreasing the density of bitou bush, with reasonable success, although it continues to spread to new locations (Hamilton et al., 2012). Regional extirpation attempts (i.e., containment) are being made at the northern and southern limits of the distribution in eastern Australia. At the northern limit, eradication is being attempted on K'gari-Fraser Island (Behrendorff et al., 2019). Populations have been substantially reduced, but there is a risk of reintroduction from uncontrolled populations on the nearby mainland (Behrendorff et al., 2019). Regional extirpation is also being attempted at the southern limit of the range in Victoria, but this may be complicated by hybridization with boneseed (Adair & Butler, 2010). In Western Australia, eradication from the entire state is being attempted and may be more feasible, although the current management program still has years to run ).

| Sampling
We sampled 119 bitou bush plants from populations across the native range in South Africa (43 individuals, 11 populations;     All leaf material was desiccated immediately after sampling with silica gel beads (Chase & Hills, 1991), samples were stored and transported at ambient temperature, and approximately 100 mg desiccated leaf from each sample was used in DNA extractions.
Whole plants were taken from the Kwinana population and grown in a quarantine glasshouse in Floreat, Western Australia, for flow cytometry.

| DNA isolations and ddRADseq library preparation
Genomic DNA was extracted from desiccated leaf material using A modified version of the ddRADseq protocol (Peterson et al., 2012)

| Assembly of RAD loci and SNP calling
All scripts used for data analysis are available at https://github.com/ asche ben/bitou_analysis. Single end reads were demultiplexed using stacks 2.1 process_radtags, (Rochette et al., 2019) with barcode rescue (−r), quality filtering (−q, −c) and read length trimming to 95 bp (−t 95). The number of reads generated per individual was even (median 6.73 M). A single individual with less than 1 M reads was removed.
Reads containing adapters were discarded using trimmomatic 0.36 (Bolger et al., 2014), and all reads without the enzyme recognition site (CGT) were also discarded. Quality checking of the filtered reads per individual (median 5.72 M) was conducted with fastqc 0.1.11 (Andrews, 2010) and multiqc 1.0 (Ewels et al., 2016). The mean per base quality (Phred +33 score) across all individuals was 36.4. Read processing results are summarized in Table S2.
For further assembly and SNP calling, individuals were split into two levels of inclusion: "bitou-pisifera" and "bitou-pisiferaboneseed." The bitou-pisifera group excluded boneseed individuals, which are genetically more distant from the other subspecies.
De novo assembly of RAD loci and SNP calling was conducted with stacks 2.1 by manually executing all steps of the denovo_map pipeline. After exploring the parameter space (Table S3) SNPs identified in the bitou-pisifera and bitou-pisifera-boneseed groups were filtered using vcftools (Danecek et al., 2011). First, all individuals with >90% missing genotypes were removed. Then, genotype calls with depth <5 were removed. Biallelic SNPs with <20% missing genotypes and minor allele frequency >0.05 were retained.
To reduce the influence of linked SNPs from the same RAD locus on population genetic analyses, a single SNP was randomly selected from each locus and the other SNPs discarded. Finally, in a postfiltering check for missingness, individuals with >50% missing genotypes were removed. By maximizing the shared SNP sites between all individuals, we aimed to increase the accuracy of our population genetic analyses (Bohling et al., 2013). Population summary statistics including pairwise F ST and AMOVA-based statistics (Φ ST and F ′ ST ) were calculated using stacks populations. Heterozygosity was calculated using vcftools.

| Phylogenetic and population structure analyses
Genetic analysis was carried out to identify relationships and structure within the bitou bush and outgroup populations. All analyses were conducted using both the bitou-pisifera-boneseed and the bitou-pisifera SNP datasets. SNPs were converted to phylip format using a Python script (Ortiz, 2019). As all sites in the multiple sequence alignment were variable SNP sites, a model with ascertainment bias correction (ASC_GTRGAMMA) was used to infer a maximum likelihood phylogeny with raxml 8.2.11 (Stamatakis, 2014) using rapid bootstrapping and 1000 bootstraps. Before the analysis, a custom Python script was used to remove all SNPs without homozygous alternate allele genotypes, as these sites are incompatible with the RAxML parameters. The tree was visualized using ggtree (Yu et al., 2017). A network analysis was also carried out to illustrate the relationships between the admixed populations. To do this, an identity-by-state (IBS) distance matrix was calculated from the SNPs using tassel 5.2.50 (Bradbury et al., 2007) with default settings. The IBS matrix was then used to carry out a NeighborNet analysis with splitstree 4.14.8 (Huson, 1998) using default settings. The resulting phylogenetic network was visualized using phangorn (Schliep, 2011). Population genetic structure was analyzed using faststructure 1.0 (Pritchard et al., 2000;Raj et al., 2014). faststructure was run for 1-10 populations (K) using the default simple prior and convergence criterion.
We selected the optimal value of K using faststructure chooseK and the method of Puechmaille (2016), which is robust for uneven sampling, implemented in structureselector (Li & Liu, 2018

| Bayesian modeling of introduction scenarios
We conducted ABC analyses to make inferences about the introduction history of bitou bush from South Africa to EAU, and to WAU. We defined five genetic groups of bitou bush based on the faststructure results, which were supported by the phylogenetic analysis and the geographical information. These groups consisted of three from the native range in South Africa: (1) (5) Western Australia (WAU: Kwinana; Table 1). We also carried out a secondary analysis using more fine-scale population structure in 1995 (Scott, Batchelor, Jucker, & Webber, 2019). We included unsampled "ghost" populations (Slatkin, 2005) in our scenarios to address the uncertainty about whether we had sampled the source population(s) of the Australian populations. Further details of parameter settings are provided in Table S4. Loci potentially under selection were removed (see "Supplementary outlier analysis" in Supporting Information).
We used the software DIYABC 2.1.0 (Cornuet et al., 2014) to generate reference tables with summary statistics based on simulated datasets. The whole set of summary statistics available in DiYaBc were applied, in addition to the linear discriminant analysis (LDA) axes. It was not necessary to use a held out set of test summary statistics, because we used an ABC random forest approach implemented in the R package aBcrf 1.8 (Pudlo et al., 2016;Raynal et al., 2019) to select the best-fitting invasion scenario. This method uses an out-of-bag error estimate to determine an error rate, which is as accurate as using a test set of the same size as the training set (Pudlo et al., 2016). Following Pudlo et al. (2016), a total of 10,000 simulated datasets per scenario were used. To ensure this number was sufficient, additional analyses with 5000 and 7000 datasets per scenario were carried out (Table S5). We estimated prior error rates for 100, 500, and 1000 trees in the random forest using the out-of-bag error calculation implemented in err.abcrf. Based on the stabilization of prior error rates at 1000 trees, we used this number for our analyses. A sequential approach was applied, comparing subsets of competing scenarios, then comparing the best-supported scenarios from each subset to determine the best scenario. This approach reduces the number of scenarios that need to be compared, while ensuring that the best scenarios always compete directly. Two preliminary analyses excluding the WAU population were carried out to compare scenarios with a single origin or an admixed origin of the EAU population. The most highly supported scenarios, that is, those receiving more than the average number of votes (trees/number of scenarios), were se-

| Ploidy-level and genome size estimation
We used two methods to estimate the ploidy-level and genome size of bitou bush. Sample ploidy was inferred based on allele frequencies at biallelic sites using nquire (Weiß et al., 2018) with a minimum allele coverage of 10 reads and a minor allele frequency of at least 0.2. Noise resulting from mismapping and other effects was scaled down using the denoise function and ploidy was inferred with lrdmodel, which uses maximum likelihood to identify the most likely fixed ploidy model (diploid, triploid, or tetraploid) compared to a free model that optimally fits the data.
We also inferred the ploidy level and genome size of bitou bush through flow cytometry of well-watered fresh leaf tissue harvested on the same day as the analysis (Doležel et al., 1989).

| Population genetic structure
The faststructure analysis of the entire bitou-boneseed-pisifera dataset reached an optimal marginal likelihood based on chooseK and the method of Puechmaille (2016) at K = 4, successfully separating the three subspecies, as well as separating bitou bush individuals from the introduced range in Australia and from the native range in South Africa ( Figure S1). When including only bitou bush in the faststructure analysis, the marginal likelihood value reached an optimum at K = 5 ( Figure S2), indicating that there were five genetic clusters of bitou bush in the analyses (Figure 2, Figure S3).
Within Australia, the WAU samples were all assigned to the same cluster with no admixture (green bars, Figure 2). The EAU samples all showed admixture between this cluster and a second cluster (purple bars, Figure 2) that was only present in EAU. The South African bitou bush individuals were separated into three geographically distinct genetic clusters in the faststructure analysis: northern, central, and southern ( Figure 2). A nested faststructure analysis of these three South African clusters revealed substructure that was almost completely consistent with the geographical sampling locations (Figures S4-S7). The only exception was that Tugela Mouth and Mtunzini were clustered as a single population.
When analyses were conducted with only four individuals from WAU, and with no individuals from WAU to prevent biases due to uneven population sampling, the optimal marginal likelihood was reached at K = 3 in both instances. However, the results were similar to the analysis with the full dataset for any given value of K.
When only EAU populations were included, the optimal marginal likelihood was reached at K = 1, although some geographic structure could be detected at higher values of K ( Figure S8). When WAU individuals were added, the optimal marginal likelihood was reached at K = 2, and some similarities could be detected between the individuals from Harvey Bay, Queensland, and the individuals from WAU ( Figure S9). and Australia, in addition to the structuring within Australia, was supported by fine scale population Ф ST comparisons (Table S6).

| Population genetic diversity
The South African populations collectively contained the highest number of private alleles, with large reductions of private alleles observed in the Australian populations (Table 3)

| Bayesian modeling of introduction scenarios
The ABC random forest analysis supported the scenario that EAU  Tables 4 and S9). The outcome of the final scenario was supported by a re-analysis using a larger dataset of 4000 SNPs (Table S10). Parameter estimation showed strong bottlenecks associated with the invasion of EAU (mean effective size of founder population = 8; 5%-95% quantiles = 5.0-13.4) and WAU (mean effective size of founder population = 9; 5%-95% quantiles = 5.0-11.1), with both estimations close to the minimum bound of 5 (Table S11)

| Ploidy and genome size evolution
Ploidy-level inference from nquire analyses found no significant deviations from expected base frequency distributions under diploidy for any samples, although ploidy of 27 samples was inferred as ambiguous ( Table S16). The results of the flow cytometry indicate that the sampled individuals were DNA diploids with only a single G1 peak observed in each replicate (Figure 4; Figure S14). The 2C DNA content of bitou bush is estimated to be 3.11 ± 0.01 pg with a 1C genome size of 1519 ± 4 Mbp (

| DISCUSS ION
In this study, we sought to determine the population genetic diversity and structure of the coastal shrub, bitou bush, and to reconstruct the species' introduction history and invasion dynamics in Australia using SNPs called from ddRADseq. Through these insights, we aimed non-native ranges, and for the management of invasive species more broadly, and these will be discussed below.

| Genetic structure and diversity
Genetic structure and diversity were consistent with recognized subspecies, and with previous phylogenetic analyses based on ISSR markers, ITS2 and DNA barcode sequencing, showing a closer relationship between bitou bush and pisifera, relative to boneseed (Barker et al., 2009;Barker et al., 2015). Within bitou bush, our SNP data showed further geographic structure within the South African

F I G U R E 4 Smoothed fluorescence histograms
showing the estimation of nuclear DNA content (2C) and ploidy for Chrysanthemoides monilifera ssp. rotundata (bitou bush) using Solanum lycopersicum (tomato) cultivar "Stupické polní rané" as a reference standard.
range (Weiss et al., 2008), with subsequent further spread through seeds from these plants.
The Australian bitou bush populations exhibited a reduction in private alleles and polymorphic loci relative to the South African populations, which are symptomatic of a genetic bottleneck occurring during founding events (Greenbaum et al., 2014). Often non-native species are found to have low genetic diversity at neutral loci relative to native populations, suggesting that high genetic diversity is not necessary for a successful invasion (e.g., Alexander et al., 2009;Hardesty et al., 2012;Hirsch et al., 2021). This can be the case even when multiple introductions have occurred (e.g., Zhu et al., 2017). Despite the decrease in bitou bush genetic diversity in the non-native range relative to the native range, there was species that is likely to be obligately outcrossed and self-incompatible (Gross et al., 2017). The ability of non-native species to thrive and adapt to new environments postgenetic bottleneck has been described as the genetic paradox of invasion Sax & Brown, 2000). We found that bitou bush has experienced a genetic bottleneck resulting in reduced genetic variation and it has survived this bottleneck without succumbing to problems associated with low genetic variation, although given the short timeframe of the WAU invasion, these effects may not have had time to arise. Our study has not tested whether bitou bush is adapting to its introduced range, and it is possible that the source population was already well-suited to the conditions in the introduced range.
We did not find evidence of polyploidy in bitou bush or related subspecies from base frequencies at biallelic SNPs or flow cytometry, although other researchers have detected varying numbers of chromosomes (Table S18). Many invasive species are polyploid, with polyploidy providing a range of benefits in a colonizing population (Baker, 1974;te Beest et al., 2012). It is therefore essential to rule of ploidy and genome size should be further tested with chromosome counts to confirm that bitou bush is a true diploid (Doležel et al., 2007).

| Introduction history
Bayesian modeling inferred introduction to Australia from an unsampled "ghost" population, likely with a strong bottleneck, albeit with only moderate posterior probability. We are not aware of any non-native bitou bush populations outside of Australia, so we infer that the source population for Australian bitou bush invasions is either an unsampled population in the native range, or is not represented by any extant population. Bitou bush populations in South Africa do exist to the north and south west along the coastline beyond our sampling extent (Barker et al., 2015). It is therefore possible that they may be the source of the seeds for the first Australian way has been assumed to be through dry ballast from shipping in the 19th and 20th centuries (Weiss et al., 2008). However, no conclusive historical evidence has been presented to support this contention.
Ports in the bitou bush native range that were shipping internationally at that time included Durban, East London, and Gqeberha (formerly Port Elizabeth). The phylogenetic analysis and PCA from this work show that the Durban population is genetically divergent from the Australian populations and an unlikely source population.
Prioritizing samples from other port areas in future molecular studies would help to test the hypothesis that dry ballast was the most likely introduction pathway to Australia.
Based on the strong bottleneck, decreased genetic diversity, and lack of genetic structure in the Australian range, we can infer that the bitou bush used for dune stabilization along the coast of New South Wales was most likely sourced from local plants, rather than from additional imports from South Africa, and that these plants were the source of further spread into Queensland. Our results also provide evidence that the EAU population served as a source population for the introduction into WAU, with genetic clustering of these populations supported by faststructure, pca and phylogenetic analysis. We note, however, that there was also support from Bayesian analysis for a scenario where the WAU population was derived from an unsampled "ghost" population.
The EAU populations are so similar that introduction from a specific location is difficult to discern. It is possible that the source population for the WAU introduction was not sampled in this study, as our sampling in EAU was only able to access material from populations between Wollongong and southern Queensland.
This sampled range left approximately 500 km of coastline unsampled between Wollongong and the southern limits of established bitou bush populations near Mallacoota, Victoria. While many of these populations have been locally extirpated, if herbarium specimens were taken before extirpation, they could play a role in future research to further refine the introduction history of bitou bush using methods such as genome skimming that are less sensitive to DNA degradation. It is likely that bitou bush was anthropogenically introduced to WAU, since the population is centered around an industrial port that had historical connections to EAU (Scott & Batchelor, 2014).

Similar secondary invasions have been documented in other non-
native plants, including Centaurea solstitialis L. (Barker et al., 2017;Eriksen et al., 2014) and Ambrosia artemisiifolia L. (van Boheemen et al., 2017). In these examples, the populations have evolved over centuries following the initial introduction, creating a bridgehead population that has adapted to local conditions, providing a fitter source population for secondary invasions. In the case of C. solstitialis, it was concluded that increased plant size has evolved following introduction (Barker et al., 2017). In contrast, bitou bush has been present in EAU only since the early 20th century (Weiss et al., 2008) and in WAU since ca. 1995 (Scott & Batchelor, 2014), representing a shorter period where the plant could adapt to local conditions prior to the secondary invasion. Rapid evolution of non-native plants is known to occur in <20 generations, resulting in strong genetic differentiation and differences in phenotype between native and introduced populations (Prentis et al., 2008;Turner et al., 2014). Our study found that non-native bitou bush populations had diverged from the native populations at neutral loci following introduction, but did not directly address whether adaptation to local conditions was occurring. The plants in WAU are exceptionally large compared to those in the native range, but this is likely the result of less pressure from specialist herbivores and diseases (i.e., enemy release hypothesis), rather than heritable trait changes (Scott, Batchelor, Jucker, & Webber, 2019). However, this is also a positive indicator for the potential to improve management by implementing classical biological control.

| Invasion management implications
The increased understanding of introduction history, spread, and genetic diversity of bitou bush across its Australian range provides four insights into how to refine the different management strategies being applied to this weed across Australia. First, from an introduction perspective, we were able to confirm that the EAU invasion originated from a small founding population from the native range in South Africa, and that subsequent EAU dune stabilization plantings were most likely carried out using locally sourced propagules, rather than new introductions from one or more locations in the native range. We can also infer that the invasion process is unlikely to be exacerbated by ongoing gene flow to WAU and EAU populations from the native range. Given that the WAU population likely has its origins from an accidental EAU introduction, we caution that interstate dispersal remains a risk within Australia. Western Australia's high border quarantine standards therefore need to be maintained, particularly from areas where bitou bush propagules could be a contaminant on imported goods. In turn, prioritizing localized extirpation around larger EAU ports would be a priority to mitigate risk at the source.
Second, up to now searches for biological control agents for bitou bush in its native range have not been guided by genetic information on the origin of the introduction. Our data points to an Eastern Cape source for bitou bush. The initial biological control studies (Adair & Scott, 1989)

sourced insects (the geometrid Comostolopsis germana
Prout) from the Port of Durban toward the northern end of the native range, to meet climate matching criteria with bitou infested areas of EAU and on the assumption that Durban was the most likely port of origin (Scott unpublished observations). While C. germana is a successful agent, many others have failed (Adair et al., 2012;Adair & Scott, 1991), indicating that the nexus between agent, host species and source should be re-examined with a focus on this newly identified region of origin.
Third, from a population invasion perspective, our findings help inform management in contrasting ways between WAU and EAU. In WAU, bitou bush is early in its invasion history, has a small population size, and studies on an EAU population show that it is an obligate outcrosser (Gross et al., 2017), which could make WAU bitou bush particularly susceptible to Allee effects. This situation also implies that invasion events must consist of at least two seeds that successfully germinate and grow to reproductive age, within cross pollination distance and are present as adult plants at the same time. All these factors are likely to combine to make reintroduction unlikely and eradication of bitou bush in WAU a realistic and feasible management goal . New plants in the WAU population are likely to come from the seed bank, rather than dispersal from the EAU population. The seed bank of bitou bush is estimated to persist for no more than 8 years and will determine the time required for eradication . In contrast in EAU, our insight reveals that genetics is not as useful for guiding optimal management choices. The low genetic structure ob-

For informing the broader biotic exchange between South
Africa and Australia, our findings on bitou bush support the application of new genomic tools to the most problematic of invasive species under management. There is the likelihood of identifying more profitable regions for targeting biological control agent searches, particularly for target species that are distributed over the broad climatic gradients that occur in these two countries.
There is also a real chance of proactively mitigating future biosecurity risks by better characterizing the pathways and propagule pressure of past introductions, given that sea and air links between the countries remain particularly strong. Finally, there is merit in considering future work that would identify suites of species with similar traits that could be tackled together to take advantage of a greater efficiency for undertaking the primary research, as well as for implementing improved management plans.

| Conclusions and future research directions
By utilizing a ddRADseq approach, we have traced the introduction history of the non-native invasive coastal plant, bitou bush, from South Africa into EAU, then to WAU, and determined that successful invasion occurred despite strong bottlenecks. Our research has revealed new knowledge on the introduction history of bitou bush, which can be applied to optimizing management. Prior to conducting genetic analysis, the source population of Australian bitou bush invasions was expected to be near Durban, and hence, searches for biocontrol agents were focussed in this area (Adair & Scott, 1989. Our study was unable to identify a specific native range source population for Australian bitou bush invasions, although there was some evidence for a source population toward the southern limits of the distribution in South Africa. To further understand the introduction history of bitou bush, more intense sampling is required at the south- Karen Bell: Conceptualization (supporting); data curation (lead); funding acquisition (supporting); investigation (supporting); methodology (lead); project administration (supporting); supervision (lead); writing -original draft (supporting); writing -review and editing (lead).